Modeling and Studying Gaming the System with Educational Data Mining

Ryan S. J. d. Baker; A. T. Corbett; I. Roll; K. R. Koedinger; V. Aleven; M. Cocea; A. Hershkovitz; A. M. J. B. de Caravalho; A. Mitrovic; M. Mathews

doi:10.1007/978-1-4419-5546-3_7

Modeling and Studying Gaming the System with Educational Data Mining

Ryan S. J. d. Baker, A. T. Corbett, I. Roll, K. R. Koedinger, V. Aleven, M. Cocea, A. Hershkovitz, A. M. J. B. de Caravalho, A. Mitrovic, M. Mathews

School of Education

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this chapter, we will discuss our work to understand why students game the system. This work leverages models of student gaming, termed “detectors”, which can infer student gaming in log files of student interaction with educational software. These detectors are developed using a combination of human observation and annotation, and educational data mining. We then apply the detectors to large data sets, and analyze the detectors’ predictions, using discovery with models methods, to study the factors associated with gaming behavior. Within this chapter, we will discuss the work to develop these detectors, and what we have discovered through these analyses based on these detectors. We will discuss evidence for how gaming the system impacts learning and evidence for why students choose to game. We will also discuss attempts to address gaming the system through adaptive scaffolding.

Original language	American English
Title of host publication	International Handbook of Metacognition and Learning Technologies
Editors	Roger Azevedo, Vincent Aleven
Place of Publication	New York, NY
Publisher	Springer New York
Pages	97-115
Number of pages	19
Volume	28
ISBN (Electronic)	9781441955463
ISBN (Print)	9781441955463
DOIs	https://doi.org/10.1007/978-1-4419-5546-3_7
State	Published - 2013

Keywords

Cognitive Tutor
Educational Data Mining (EDM)
Educational Software
Interactive Learning Environment
Tutoring System

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-1-4419-5546-3_7

Cite this

Baker, R. S. J. D., Corbett, A. T., Roll, I., Koedinger, K. R., Aleven, V., Cocea, M., Hershkovitz, A., de Caravalho, A. M. J. B., Mitrovic, A., & Mathews, M. (2013). Modeling and Studying Gaming the System with Educational Data Mining. In R. Azevedo, & V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (Vol. 28, pp. 97-115). Springer New York. https://doi.org/10.1007/978-1-4419-5546-3_7

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title = "Modeling and Studying Gaming the System with Educational Data Mining",

abstract = "In this chapter, we will discuss our work to understand why students game the system. This work leverages models of student gaming, termed “detectors”, which can infer student gaming in log files of student interaction with educational software. These detectors are developed using a combination of human observation and annotation, and educational data mining. We then apply the detectors to large data sets, and analyze the detectors{\textquoteright} predictions, using discovery with models methods, to study the factors associated with gaming behavior. Within this chapter, we will discuss the work to develop these detectors, and what we have discovered through these analyses based on these detectors. We will discuss evidence for how gaming the system impacts learning and evidence for why students choose to game. We will also discuss attempts to address gaming the system through adaptive scaffolding.",

keywords = "Cognitive Tutor, Educational Data Mining (EDM), Educational Software, Interactive Learning Environment, Tutoring System",

author = "Baker, {Ryan S. J. d.} and Corbett, {A. T.} and I. Roll and Koedinger, {K. R.} and V. Aleven and M. Cocea and A. Hershkovitz and {de Caravalho}, {A. M. J. B.} and A. Mitrovic and M. Mathews",

year = "2013",

doi = "10.1007/978-1-4419-5546-3_7",

language = "American English",

isbn = "9781441955463",

volume = "28",

pages = "97--115",

editor = "Roger Azevedo and Vincent Aleven",

booktitle = "International Handbook of Metacognition and Learning Technologies",

publisher = "Springer New York",

address = "United States",

}

Baker, RSJD, Corbett, AT, Roll, I, Koedinger, KR, Aleven, V, Cocea, M, Hershkovitz, A, de Caravalho, AMJB, Mitrovic, A & Mathews, M 2013, Modeling and Studying Gaming the System with Educational Data Mining. in R Azevedo & V Aleven (eds), International Handbook of Metacognition and Learning Technologies. vol. 28, Springer New York, New York, NY, pp. 97-115. https://doi.org/10.1007/978-1-4419-5546-3_7

Modeling and Studying Gaming the System with Educational Data Mining. / Baker, Ryan S. J. d.; Corbett, A. T.; Roll, I. et al.
International Handbook of Metacognition and Learning Technologies. ed. / Roger Azevedo; Vincent Aleven. Vol. 28 New York, NY: Springer New York, 2013. p. 97-115.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Modeling and Studying Gaming the System with Educational Data Mining

AU - Baker, Ryan S. J. d.

AU - Corbett, A. T.

AU - Roll, I.

AU - Koedinger, K. R.

AU - Aleven, V.

AU - Cocea, M.

AU - Hershkovitz, A.

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AU - Mitrovic, A.

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AB - In this chapter, we will discuss our work to understand why students game the system. This work leverages models of student gaming, termed “detectors”, which can infer student gaming in log files of student interaction with educational software. These detectors are developed using a combination of human observation and annotation, and educational data mining. We then apply the detectors to large data sets, and analyze the detectors’ predictions, using discovery with models methods, to study the factors associated with gaming behavior. Within this chapter, we will discuss the work to develop these detectors, and what we have discovered through these analyses based on these detectors. We will discuss evidence for how gaming the system impacts learning and evidence for why students choose to game. We will also discuss attempts to address gaming the system through adaptive scaffolding.

KW - Cognitive Tutor

KW - Educational Data Mining (EDM)

KW - Educational Software

KW - Interactive Learning Environment

KW - Tutoring System

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BT - International Handbook of Metacognition and Learning Technologies

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PB - Springer New York

CY - New York, NY

ER -

Modeling and Studying Gaming the System with Educational Data Mining

Abstract

Keywords

UN SDGs

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